Learning-based Classification Research Articles

Prediction of cardiovascular events after carotid endarterectomy using pathological images and clinical data

Abstract Purpose Carotid endarterectomy (CEA) is a surgical treatment for carotid artery stenosis. After CEA, some patients experience cardiovascular events (myocardial infarction, stroke, etc.); however, the prognostic factor has yet to be revealed. Therefore, this study explores the predictive factors in pathological images and predicts cardiovascular events within one year after CEA using pathological images of carotid plaques and patients’ clinical data. Method This paper proposes a two-step method to predict the prognosis of CEA patients. The proposed method first computes the pathological risk score using an anomaly detection model trained using pathological images of patients without cardiovascular events. By concatenating the obtained image-based risk score with a patient’s clinical data, a statistical machine learning-based classifier predicts the patient’s prognosis. Results We evaluate the proposed method on a dataset containing 120 patients without cardiovascular events and 21 patients with events. The combination of autoencoder as the anomaly detection model and XGBoost as the classification model obtained the best results: area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1-score were 81.9%, 84.1%, 79.1%, 86.3%, and 76.6%, respectively. These values were superior to those obtained using pathological images or clinical data alone. Conclusion We showed the feasibility of predicting CEA patient’s long-term prognosis using pathological images and clinical data. Our results revealed some histopathological features related to cardiovascular events: plaque hemorrhage (thrombus), lymphocytic infiltration, and hemosiderin deposition, which will contribute to developing preventive treatment methods for plaque development and progression.

Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review

Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

A Specialized Pipeline for Efficient and Reliable 3D Semantic Model Reconstruction of Buildings from Indoor Point Clouds.

Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline's efficiency and accuracy offer valuable contributions to future advancements in point cloud processing.

Estimating afforestation related forest cover change using data fusion and machine learning

Abstract Climate change threatens natural ecosystems, including biodiversity, and has emerged as the greatest challenge to human well-being. To support sustainable forest use, it is important to accurately map the distribution of afforestation and how the forests have changed over time. Climate change is affected by land use and land cover (LULC) since it alters biogeochemical cycles. Pakistan's declining forest cover has motivated policymakers to undertake the Green Growth Initiative (GGI). In accordance with its commitment to the Bonn Challenge, Pakistan executed the Billion Tree Afforestation Project (BTAP) to restore forests covering approxi-mately 0.35 million Ha. To estimate forest cover mapping accuracy, we developed a novel method to combine Sentinel-1, Sentinel-2 data, and vegetation indices to assess forest cover change. The methods and models were used to create forest maps for the period of 2016 to 2022. The optimal mapping approach was chosen after classifying the land cover using two machine learning-based classifiers: random forests (RF) and support vector machines (SVM). Finally, the spatiotemporal distribution of the changes induced by afforestation was generated. Based on the classifications of land cover, forest and non-forest identification was carried out. The results show that RF produces more accurate results, with kappa coefficient of 94-97% & 0.93-0.96 respectively. The classification accuracy and Kappa coefficient of the SVM model ranges from 92-94% & 0.91-0.95. Overall, forested areas in the study area increased by 3.7%. The techniques used in this study are cost-effective for accurately monitoring changes in forest cover.

Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier’s performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.

Machine learning-based classification of valvular heart disease using cardiovascular risk factors

Valvular Heart Disease (VHD) is a globally significant cause of mortality, particularly among aging populations. Despite advancements in percutaneous and surgical interventions, there are still uncertainties that remain regarding the risk factors that significantly contribute to this condition within the domain of cardiovascular disease. This study investigates these uncertainties and the role of machine learning in categorizing VHD based on cardiovascular risk factors. It follows a two-part investigation comprising feature extraction and classification phases. Feature extraction is initially performed using a wrapping approach and refined further with binary logistic regression. The second phase employs five classifiers: Artificial Neural Network (ANN), XGBoost, Random Forest (RF), Naïve Bayes, and Support Vector Machine (SVM), along with advanced methods such as SVM combined with Principal Component Analysis (PCA) and a majority-voting ensemble method (MV5). Data on VHD cases were collected from DHQ Hospital Faisalabad using simple random sampling. Various statistical measures, such as the ROC curve, F-measure, sensitivity, specificity, accuracy, MCC, and Kappa are applied to assess the results. The findings reveal that the combination of SVM with PCA achieves the highest overall performance while the MV5 ensemble method also demonstrates high accuracy and balance in sensitivity and specificity. The variation in VHD prevalence linked to specific risk factors highlights the importance of a comprehensive approach to reduce this disease’s burden. The Exceptional performance of SVM + PCA and MV5 highlights their significance in diagnosing VHD and advancing knowledge in biomedicine.

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaque rupture. Manual classification is time-consuming and labor-intensive. Several machine learning-based classification approaches have been proposed and evaluated in recent years. In the current study, we develop a novel pipeline composed of serial classifiers for distinguishing IVUS images into five categories: normal, calcified plaque, attenuated plaque, fibrous plaque, and echolucent plaque. The cascades comprise densely connected classification models and machine learning classifiers at different stages. Over 100,000 IVUS frames of five different lesion types were collected and labeled from 471 patients for model training and evaluation. The overall accuracy of the proposed classifier is 0.877, indicating that the proposed framework has the capacity to identify the nature and category of coronary plaques in IVUS images. Further, it may provide real-time assistance on plaque identification and facilitate clinical decision-making in routine practice.

Explainable olive grove and grapevine pest forecasting through machine learning-based classification and regression

Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H2O, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.

Enhancing adaptivity of active sonar classifier considering loss landscape under dataset shift

We study the loss landscape of deep neural networks and use it to solve dataset shift problems in active sonar classification. The dataset shift degrades the generalization performance of supervised learning-based classifiers. When test data samples are available, fine-tuning methods can be used to mitigate the performance decrease. However, they can induce catastrophic forgetting and negative transfer because fine-tuned weights could be overfitted to the test data distribution. These problems are more severe in active sonar datasets with small amounts and less diversity. To mitigate the two problems in conventional fine-tuning, we explore the loss landscape of supervised learning-based active sonar classifiers and apply it to derive adaptative weights from pre-trained weights. Our results showed promising performance.

Machine Learning-Based Classification System for Tuberculosis Detection Using Locally Collected Radiographs

Machine Learning-Based Classification System for Tuberculosis Detection Using Locally Collected Radiographs

Machine learning-based classification of varicocoele grading: A promising approach for diagnosis and treatment optimization.

Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography-based varicocoele grading. We aimed to develop a multi-class machine learning model for the grading of varicocoeles based on ultrasonographic measurements. Between January and May 2024, we enrolled unilateral varicocoele patients at an infertility clinic, assessing their varicocoele stages using the Dubin and Amelar system. We measured vascular diameter and reflux time at the testicular apex and the subinguinal region ultrasonography in both the supine and standing positions. Using these measurements, we developed four multi-class machine learning models, evaluating their performance metrics and determining which patient position and projection were most influential in varicocoele grading. We included 248 patients with unilateral varicocoele in the study, their average age was 26.61±4.95 years old. Of these, 212 had left-sided and 36 had right-sided varicocoeles. According to the Dubin and Amelar system, there were 66 grade I, 96 grade II, and 86 grade III varicocoeles. Among the models we created, the random forest (RF) model performed best, with an overall accuracy of 0.81±0.06, an F1 score of 0.79±0.02, a sensitivity of 0.69±0.02, and a specificity of 0.8±0.03. Vascular diameter measurement at the testicular apex in the supine position had the most impact on grading across all models. In support vector machine and multi-layer perceptron models, reflux time measurements from the subinguinal projection in the standing position contributed the most, while in RF and k-nearest neighbors models, measurements from the subinguinal projection in the supine position were the most influential. Machine learning methods have demonstrated superior accuracy in predicting disease compared to traditional statistical regressions and nomograms. These advancements hold promise for clinically automated prediction of varicocoele grades in patients. Tailored varicocoele grading for individuals has the potential to enhance treatment effectiveness and overall quality of life.

Primary Hyperhidrosis and Sensitive Skin: Exploring the Link with Predictive Machine Learning-Based Classification Models.

Primary hyperhidrosis (PHH) is a disorder of excessive sweating caused by aberrant cholinergic signaling. Sensitive skin (SS) is a condition of subjective cutaneous hyperreactivity to innocuous stimuli, impacting 40% to 70% of the population. SS is exacerbated by sweat, stress, and heat, suggesting that cholinergic stimulation may contribute to SS flares. To survey PHH sufferers to assess hyperhidrosis (HH) and SS symptom burden. An International Review Board (IRB)-exempt survey was disseminated by the International Hyperhidrosis Society. A predictive classification model for SS was built using random forest machine learning algorithms. Of the 637 respondents with PHH, 89% reported SS; and there was a significant association between HH and SS severity scores. Importantly, SS occurred on body sites affected and unaffected by HH. Predictive modeling designated Sensitive Scale-10 (SS-10), a validated questionnaire to gauge SS severity, to be the most helpful in predicting SS in this cohort. Self-reported data. These data are the first to propose and support a relationship between SS and HH. SS occurred with greatest frequency at HH-afflicted body sites, but also occurred on unaffected sites, suggesting that sweat is not the sole causative link. Future work can explore cholinergic signaling as a potential link between these conditions. Screening HH patients for SS may be warranted. J Drugs Dermatol. 2024;23(10):882-888. doi:10.36849/JDD.8461.

A-114 Deep learning-based classification of early-stage mycosis fungoides and benign inflammatory dermatoses on hematoxylin and eosinstained whole-slide images: a retrospective, proof-of-concept study

A-114 Deep learning-based classification of early-stage mycosis fungoides and benign inflammatory dermatoses on hematoxylin and eosinstained whole-slide images: a retrospective, proof-of-concept study

Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe

The analysis of sports movements is of great importance for optimizing sports performance, minimizing injury risks, and ensuring that athletes work with correct techniques. Powerlifting is a power sport consisting of fundamental movements such as bench press, deadlift, and squat. These movements are inherently complex and challenging to execute. Therefore, it is of great importance to perform these movements with the correct technique and safely. The aim of this study was to classify these movements using deep learning methods to ensure that the basic movements in powerlifting sports (bench press, deadlift, and squat) are applied with correct techniques and to minimize the risk of injury. In this study, feature extraction was performed on powerlifting movements using the deep learning-based SqueezeNet model, followed by classification using machine learning methods. The dataset was compiled from 876 images of bench press, deadlift, and squat movements sourced from various online platforms. Additionally, the dataset was expanded through data augmentation techniques, and key points of posture estimation were added to the images using the Mediapipe library. The obtained datasets were classified using Neural Network, Logistic Regression, Support Vector Machine and Random Forest algorithms and model performances were evaluated using various metrics. The findings revealed that the Neural Network model demonstrated superior performance, achieving the highest accuracy (0.989). Additionally, the integration of pose estimation and data augmentation techniques significantly enhanced classification accuracy and overall model performance. The findings of this study show that deep learning methods are powerful tools in sports movement analysis and can make significant contributions to the evaluation of athletes' performance.

Deep Learning-Based Classification of Fruit Diseases - A Comprehensive Approach to Automated Detection and Diagnosis

This study aims to develop a deep learning-based model for accurate fruit disease classification using advanced architectures like ResNet50 and VGG19. The dataset includes images of fruits such as grapes, mangoes, lemons, pomegranates, and guavas, with both healthy and diseased categories. Diseases like black rot in grapes and bacterial canker in mangoes are considered for classification. The objective is to build a model capable of accurately identifying these diseases to aid in early identification and control. The approach begins with pre-processing the photographs using standardisation, resizing, and data augmentation techniques including flipping, zooming, and rotation to strengthen the model. By enhancing picture contrast with Contrast Limited Adaptive Histogram Equalisation (CLAHE), important illness characteristics can be better highlighted. We train and evaluate deep learning models by dividing the dataset into three parts: training, validation, and test. The ratio of the parts is 80:10:10. According on the results, the ResNet50 model outperforms all other accuracy at 99.77%.

Classification of Lung Cancer using Pre-Trained Deep Learning Models

Many Computer Assisted Diagnosis (CAD) systems have been designed and used in recent past for diagnosingdifferenttypes of cancer. Identification of carcinoma at an earlier stage is more important, and it is made possible due to the use modern image processing and deep learning methods.The occurrence of Lung cancer is seen to be increased and Computed Tomography (CT) scan images were utilized in investigation to locate and classify lung cancer, also for determining the severity of those cancer. This study is aimed at employing pre-trained deep neural networks for classification of lung cancer images. A gaussian-based approach is used to segment CT scan images. In this research exploits a transfer learning-based classification method for the chest CT images acquired from Cancer Image Archive and available in the Kaggle platform. Pre- trained models VGG and RESNET were trained using segmented chest CT images, and their performance was evaluated using different optimization algorithms were analyzed. Keywords: Computer Aided Diagnosis, lung cancer, deep learning, CT image, gaussian, transfer learning, pre-trained models, optimization algorithms.

Solving the imbalanced dataset problem in surveillance image blur classification

Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection and classification of the blur anomalies in the video are crucial to these systems. Traditional learning-based classification methods often face imbalance problems in the sample numbers and distributions among the data classes in the dataset that severely affect their training and hence the classification performance. In this paper, a new learning-based approach for surveillance image blur classification is proposed. The imbalanced dataset problem is tackled both at the data and algorithm levels. At the data level, two synthesizers are developed to generate the required negative surveillance images to balance the sample numbers for all classes. At the algorithm level, an attention-based structure making use of the special feature of the minority class is proposed to improve the classification accuracy. Our experiment results show that the proposed approach significantly outperforms state-of-the-art methods for blur classification while keeping the model size small for edge applications.

Cross-Domain Image Classification via Multimodal Augmentations and Bacterial Foraging Optimization

Identification of diseases from body scans requires design of pre-processing, filtering, segmentation, feature representation, classification & post-processing operations. Existing deep learning-based classification models use context-specific segmentation followed by convolutions or transformer-based classification techniques. However, the majority of these models do not perform correlative analysis between different disease types. This limitation restricts their scalability and applicability in clinical scenarios. To address these limitations, this research work proposed IMAC-MOC, a model that integrates multimodal augmentations to enhance performance in cross-domain and multi-organ image classification. The proposed model initially collects large-scale scans at organ-level like MRI, CT scan, Kidney Scans, etc. and represents them via multimodal feature sets. These feature maps are combined and processed via a Bacterial Foraging Optimizer (BFO) which assists in identification of high variance inter-class feature sets.

Machine Learning-Based Classification of Body Imbalance and Its Intensity Using Electromyogram and Ground Reaction Force in Immersive Environments

Machine Learning-Based Classification of Body Imbalance and Its Intensity Using Electromyogram and Ground Reaction Force in Immersive Environments